Measurement Error, Missing Data and Semiparametrics

描述(由申请人提供)：这份建议书反映了我们继续 对解决测量误差、一般缺失数据的问题感兴趣 回归设置。提出的研究课题自然源于两个方面 重要研究： (I)结肠癌致瘤项目，该项目包括一系列 在细胞水平上研究结肠癌发生的实验，以及(Ii) 前列腺癌结果研究，这是最大的多中心观察 非控制性人群医疗行为之间联系的调查研究 男性真实环境与健康相关生活质量(HRQOL) 被诊断出患有前列腺癌。 我们建议研究五个主要研究课题： 发展有效的广义估计方程(GEE)程序 和半参数边际回归模型。 估计两个变量之间的动态相关性，如细胞凋亡 死亡)和DNA加合物损伤，作为连续协变量细胞的函数 在结肠隐窝里的位置。 开发适应新型协变量测量的方法 使用来自次级混合模型的预测作为 主模型中的协变量。 利用多种倍数实现不完全数据的容错分析 归罪程序，包括非参数和半参数归罪。 采用近似鞍点方法对符合以下条件的统计推断 适用于分析来自少数对象/集群/单位的数据。 该提案的主要重点是开发高效、易于计算的 方法，而不强加不必要的参数假设。特别强调 将提供给从纵向和纵向收集的相关观测 集群式研究。

DESCRIPTION (provided by applicant): This proposal reflects our continuing interest in solving problems of measurement error, missing data in general regression settings. The proposed research topics naturally arise from two important studies: (i) Colon Cancer Tumorigenesis Project, which consists of a series of experiments to study colon cancer tumorigenesis at the cellular level, and (ii) Prostate Cancer Outcomes Study, which is the largest multicenter observational study to investigate the link between medical practices in the uncontrolled real-world environment and health-related quality of life (HRQOL) among men diagnosed with prostate cancer. We propose to study five primary research topics: Developing efficient generalized estimating equation (GEE) procedure under non- and semi-parametric marginal regression models. Estimating a dynamic correlation between two variables, such as apoptosis (cell death) and DNA adduct damage, as a function of a continuous covariate, cell positions within a colon crypt. Developing methodologies to accommodate a new type of covariate measurement error problem caused by using predictions from a secondary mixed model as the covariate in the primary model. Accommodating analyses of incomplete data through utilizing various multiple imputation procedures including non- and semi-parametric imputations. Adopting approximate saddle point methods to statistical inferences that are suitable to analyze data from a small number of subjects/clusters/units. The major focus of the proposal is development of efficient, easily calculable methods without imposing unnecessary parametric assumptions. Special emphasis will be given to correlated observations collected from longitudinal and clustered studies.